基于自适应逐次II型截尾样本下EIG分布的参数统计推断

2019-09-10 07:22季丹丹闫在在
赤峰学院学报·自然科学版 2019年3期

季丹丹 闫在在

摘要:近几年,针对缺失数据的处理这方面的应用研究大量涌现,使得缺失数据下的可靠性理论迅速发展.而在可靠性试验和寿命试验中,截尾方案能在试验所花费的总时间、单元个数和基于试验结果的统计推断效率之间取得平衡.在这种情况下,一种自适应的截尾方案被提出来,并且被许多专家学者研究应用.因此本文讨论,基于自适应逐次II型截尾样本,提出了EIG分布的统计推断理论等问题.对于未知参数,提出了极大似然估计(MLEs).利用MLEs的渐近正态性得到参数的近似置信区间.并运用一组真实数据进行模拟讨论.

关键词:EIG分布;截尾数据;极大似然估计;自适应逐次II型截尾

中图分类号:O212 文献标识码:A 文章编号:1673-260X(2019)03-0013-05

1 引言

许多情形下,考虑到费用和时间的原因,寿命测试验通常在所有测试单元都失败前终止.这种情况下,人们只能得到部分样本的失效时间,这些数据即为截尾数据.在过去的50年里,一些专家学者已经在研究和讨论基于截尾样本的参数统计推断问题.最常见的截尾方案大体分两种,I型(定时)截尾和II型(定量)截尾.其中I型截尾表示寿命试验在规定的时间T内终止,II型截尾则表示寿命试验在第m次失效时终止,其中m是提前设定的.逐次II型截尾方案是II型截尾方案的推广形式,表示假设有n个单元置于寿命试验中,而只有m个失效单元被观测到.在观测到第一个失效单元时,在剩余的未失效单元中随机移除R1个单元.同样的,在观测到第二个失效时间时,R2个单元被随机移除.寿命试验将在m个失效单元都被观测到终止,最后将Rm=n-R1-R2-…-Rm-1个未失效单元全部移除.产生逐次型截尾样本数据的原因很多,如有些航空航天、核反应堆等零部件,其试验消耗成本过高,为节约时间和费用,通过检验后,人们通常会在未失效的产品中取出一部分作为他用.这样即节约了成本又知道了产品的特性.再如,对某些产品进行跟踪调查时,出于某些原因,使得一些使用者在某个时间后失联,因而我们对这批产品也就只掌握了部分数据.对于逐次截尾的广泛的回顾与讨论,读者们可以参考Aggarwala(1998)[1]、alakrishnan(2008)[2]、Fernandez(2004)[3]、Soliman(2008)[4]和Chansoo K和Keunhee H(2009)[5].

2 自适应逐次II型截尾试验

Ng et al.[7]提出一个自适应逐次II型截尾方案,它是I型截尾和II型逐次截尾的混合,既节约了试验成本,又增加了统计分析效率.

6 结语

本文介绍了截尾樣本的由来及种类,并由广义逐次II型截尾试验,引入并阐述了自适应逐次II型截尾试验的实施过程.由于截尾数据的广泛应用性,本文基于自适应逐次II型截尾样本,讨论了EIG分布所含参数的极大似然估计和近似置信区间,并运用真实例子模拟讨论.

参考文献:

〔1〕Aggarwala R., Balakrishnan N.. Some properties of progressive censored order statistics from arbitrary and uniform distributions with applications to inference and simulation[J]. Statist. Plann. Inference, 1998,70(1):35-49.

〔2〕Balakrishnan N., Anna Dembinska. Progressively Type-II right censored order statistics from discrete distributions[J]. Journal of Statistical Planning and Inference,2008,138(4):845–856.

〔3〕Fernandez A. J. On estimating exponential parameters with general type-II progressive censoring[J]. Journal of Statistical Planning and Inference, 2004,121(1):135-147.

〔4〕Soliman, Ahmed A. Estimations for pareto model using general progressive censored data and symmetric loss[J]. Communications in statistics-theory and methods, 2008,37(9):1353-1370.

〔5〕Chansoo K., Keunhee H. Estimation of the scale parameter of the Rayleigh distribution under general progressive censoring[J]. Journal of the Korean Statistical Society, 2009,38(3):239-246.

〔6〕季丹丹.一种拓展的逆高斯分布的性质及应用[D].内蒙古:内蒙古工业大学,2017.

〔7〕D. Kundu, A. Joarder, Analysis of Type-II progressively hybrid censored data[J], Comput. Stat. Data Anal. 2006, (50) 2258–2509.

〔8〕H.K.T. Ng, D. Kundu, P.S. Chan, Statistical analysis of exponential lifetimes under an adaptive Type-II progressive censoring scheme[J], Naval Res. Logist.2009, (56) 687–698.

〔9〕Rezapour M., Alamatsaz M. H. On properties of progressively Type-II censored order statistics arising from dependent and non-identical random variables[J]. Statistical Methodology, 2013,10(1):58-71.

〔10〕Mashail M. AL Sobhi, Ahmed A. Soliman. Estimation for the exponentiated Weibull model with adaptive Type-II progressive censored schemes[J]. Applied Mathematical Modelling, 2016,40(2):1180–1192.

〔11〕Nassar M. Estimation of the inverse Weibull parameters under adaptive type-II progressive hybrid censoring scheme[J]. Journal of Computational and Applied Mathematics,2017,315:228–239.

〔12〕魏宗舒.概率論与数理统计教程[M].北京:高等教育出版社,2008.

〔13〕N.Balakrishnan, Rita Aggarwala, Progressive Censoring Theory,methods and Applications[M]. Statistics for industry and technology, 1956.

〔14〕Rezaei S, Tahmasbi R, Mahmoodi M. Estimation of P[Y < X] for generalized Pareto distribution [J]. J Statist Plan Inference. 2010,140:480-494.

〔15〕Greene W H. Econometric Analysis: Fourth Edition [C]. Upper Saddle River, NJ. 2000.

〔16〕Alan A. Categorical Data Analysis (2nd Ed.) [J]. Journal of the Royal Statistical Society, 2002, 40(4).

〔17〕Valiollahi R, Asgharzadeh A, Raqab MZ.Estimation of P[Y

〔18〕Saracoglua B, Kinacia I, Kundu D. (2012) On estimation of R=P[Y

〔19〕 Childs A, Chandrasekhar B, Balakrishnan N, Kundu D.Exact inference based on type-I and type-II hybrid censored samples from the exponential distribution[J]. Ann Inst Stat Math 2003,55:319-330.

〔20〕Balakrishnan,Cramer,Kamps. Bounds for Means and Variances of Progressive Type II Censored Order Statistics[J]. Statist Probab. Lett.2001,54,301-315.

〔21〕Balakrishnan,N.,Cramer,E.,Progressive censoring from heterogeneous distributions with applications to robustness[J]. Ann.Inst.Statist.Math.2008,60:151-171.

〔22〕Guilbaud. Exact non-parametric confidence intervals for quantiles with progressive type-II censoring[J].Scand.J. Statist. 2001,28:699-713.

〔23〕Guilbaud O., Exact non-parametric confidence, prediction and tolerance intervals with progressive type-II censoring[J]. Scand. J.Statist.2004,31:265–281.

〔24〕U Balasooriya, N Balakrishnan. Reliability sampling plans for lognormal distribution based on progressively censored

Samples[J]. IEEE Trans. Reliab. 2000,49:199–203.